The Information and Communication Technology (ICT) industry is one of the most energy consuming industries in the world. With the increase of the number of global mobile traffic users which is growing rapidly for each year, it is more important than ever for all industries to implement energy efficient methods to decrease the greenhouse gas emissions. This thesis looks into Ericsson, one of the largest companies within ICT, and how their energy efficient methods in LTE impact the end-users. The experiment was conducted in an Ericsson laboratory in Lund and the results showed that some quality of service metrics (latency,throughput, etc.) were impacted by the different energy efficient features enabled. However, performing a mean opinion score showed that the end-users were not as affected while browsing through different sites and streaming videos in 720p. While the experiment was performed on a smaller scale (4 User Equipments and 3 end-users) the result was promising enough for it to be conducted on a larger scale in the future. With enough data Ericsson and other ICT companies can be able to convince mobile operators to enable more energy efficient features (without it having any impact on the end-users) while fighting the climate change.

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Arbetet undersöker huruvida en traditionell multi-page applikation eller en AJAX baserad single-page applikation presterar bäst med hänsyn till quality of service där insamling och analys av svarstider nyttjas för att undersöka frågan. Arbetet specificerar vidare på M-commerce där mobila enheter uppkopplade mot mobila nätverk ligger ifokus. Arbetet utförs som ett experiment där en multi-page samt en single-page e-handels applikation med samma utseende och funktionalitet utvecklas, varpå svarstider samlas in genom att automatisk bläddra genom en mängd produkter via pagination där svarstiderna loggas. Mätningar utfördes för nätverken WiFi, 4G samt 3G med varierande datamängd. Resultaten för applikationerna jämförs sedan med hjälp av diagram och ANOVA tester som resulterade i slutsatsen att single-page visade sig inneha de kortaste svarstiderna i 8/9 mätserier. I framtiden vore det även intressant att bland annat undersöka flera funktionaliteter hos applikationen som kundvagnen, sökmotorn och navigation mellan produkttyper för att framställa ett mer fullständigt resultat. / <p>Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.</p>

Data sensing and retrieval in WSNs have a great applicability in military, environmental, medical, home and commercial applications. In query-based WSNs, a user would issue a query with QoS requirements in terms of reliability and timeliness, and expect a correct response to be returned within the deadline. Satisfying these QoS requirements requires that fault tolerance mechanisms through redundancy be used, which may cause the energy of the system to deplete quickly. This dissertation presents the design and validation of adaptive fault tolerant QoS control algorithms with the objective to achieve the desired quality of service (QoS) requirements and maximize the system lifetime in query-based WSNs. We analyze the effect of redundancy on the mean time to failure (MTTF) of query-based cluster-structured WSNs and show that an optimal redundancy level exists such that the MTTF of the system is maximized. We develop a hop-by-hop data delivery (HHDD) mechanism and an Adaptive Fault Tolerant Quality of Service Control (AFTQC) algorithm in which we utilize "source" and "path" redundancy with the goal to satisfy application QoS requirements while maximizing the lifetime of WSNs. To deal with network dynamics, we investigate proactive and reactive methods to dynamically collect channel and delay conditions to determine the optimal redundancy level at runtime. AFTQC can adapt to network dynamics that cause changes to the node density, residual energy, sensor failure probability, and radio range due to energy consumption, node failures, and change of node connectivity. Further, AFTQC can deal with software faults, concurrent query processing with distinct QoS requirements, and data aggregation. We compare our design with a baseline design without redundancy based on acknowledgement for data transmission and geographical routing for relaying packets to demonstrate the feasibility. We validate analytical results with extensive simulation studies. When given QoS requirements of queries in terms of reliability and timeliness, our AFTQC design allows optimal "source" and "path" redundancies to be identified and applied dynamically in response to network dynamics such that not only query QoS requirements are satisfied, as long as adequate resources are available, but also the lifetime of the system is prolonged. / Ph. D.

The rapid evolution of portable devices and social media has enabled pervasive forms of distributed cooperation. A group could perform a task using a heterogeneous set of the devices (desktop, mobile), connections (wireless, wired, 3G) and software clients. We call this form of systems Distributed Dynamic Cooperative Environments (DDCEs). Content in DDCEs is created and shared by the users. The content could be static (e.g., video or audio), dynamic (e.g.,wikis), and/or Objects with behavior. Objects with behavior are programmed objects that take advantage of the available computational services (e.g., cloud-based services). Providing a desired Quality of Experience (QoE) in DDCEs is a challenge for cooperative systems designers. DDCEs are expected to provide groups with the utmost flexibility in conducting their cooperative activities. More flexibility at the user side means less control and predictability of the groups' behavior at the system side. Due to the lack of Quality of Service (QoS) guarantees in DDCEs, groups may experience changes in the system behavior that are usually manifested as delays and inconsistencies in the shared state. We question the extent to which cooperation among group members is sensitive to system changes in DDCEs. We argue that a QoE definition for groups should account for cooperation emergence and sustainability. An experiment was conducted, where fifteen groups performed a loosely-coupled task that simulates social traps in a 3D virtual world. The groups were exposed to two forms of system delays. Exo-content delays are exogenous to the provided content (e.g., network delay). Endo-content delays are endogenous to the provided content (e.g., delay in processing time for Objects with behavior). Groups' performance in the experiment and their verbal communication have been recorded and analyzed. The results demonstrate the nonlinearity of groups' behavior when dealing with endo-content delays. System interventions are needed to maintain QoE even though that may increase the cost or the required resources. Systems are designed to be used rather than understood by users. When the system behavior changes, designers have two choices. The first is to expect the users to understand the system behavior and adjust their interaction accordingly. That did not happen in our experiment. Understanding the system behavior informed groups' behavior. It partially influenced how the groups succeeded or failed in accomplishing its goal. The second choice is to understand the semantics of the application and provide guarantees based on these semantics. Based on our results, we introduce the following design guidelines for QoE provision in DDCEs. • If possible the system should keep track of information about group goals and add guarding constraints to protect these goals. • QoE guarantees should be provided based on the semantics of the user-generated content that constitutes the group activity. • Users should be given the option to define the content that is sensitive to system changes (e.g., Objects with behavior that are sensitive to delays or require intensive computations) to avoid the negative impacts of endo-content delays. • Users should define the Objects with behavior that contribute to the shared state in order for the system to maintain the consistency of the shared state. • Endo-content delays were proven to have significantly negative impacts on the groups in our experiment compared to exo-content delays. We argue that system designers, if they have the choice, should trade processing time needed for Objects with behavior for exo-content delay. / Ph. D.

This research addresses the concept of radio resource allocation for cellular communications systems operating in congested and contested environments with an emphasis on end-to-end quality of service (QoS). The radio resource allocation is cast under a proportional fairness formulation which translates to a convex optimization problem. Moreover, the resource allocation scheme considers subscription-based and traffic differentiation in order to meet the QoS requirements of the applications running on the user equipment in the system. The devised resource allocation scheme is realized through a centralized and a distributed architecture and solution algorithms for the aforementioned architectures is derived and implemented in the mobile devices and the base stations. The sensitivity of the resource allocation scheme to the temporal dynamics of the quantity of the users in the system is investigated. Furthermore, the sensitivity of the resource allocation scheme to the temporal dynamics in the application usage percentages is accounted for. In addition, a transmission overhead of the centralized and distributed architectures for the resource allocation schemes is performed. Furthermore, the resource allocation scheme is modified to account for a possible additive bandwidth done through spectrum sharing in congested and contested environments, in particular spectrally coexistent radar systems. The radar-spectrum additive portion is devised in a way to ensure fairness of the allocation, high bandwidth utilization, and interference avoidance. In order to justify the aforesaid modification, the interference from radar systems into the Long Term Evolution (LTE) as the predominant 4G technology is studies to confirm the possibility of the spectrum sharing. The preceding interference analysis contains a detailed simulation of radar systems, propagation path loss models, and a third generation partnership project compliant LTE system. The propagation models are Free Space Path Loss (FSPL) and Irregular Terrain Model (ITM). The LTE systems under consideration are macro cell, outdoor small cells, and indoor small cells. Furthermore, the resource allocation under channel consideration is formalized such that the resources are allocated under a congested environment and based on the quality of channel the users have in the network as well as the quality of service requirements of the applications running on the mobile devices. / Ph. D.

Nowadays, implementing a complex system, which executes various applications with different levels of assurance, is a growing trend in modern embedded real-time systems to meet cost, timing, and power consumption requirements. Medical devices, automotive, and avionics industries are the most common safety-critical applications, exploiting these systems known as Mixed-Criticality (MC) systems. MC applications are real-time, and to ensure the correctness of these applications, it is essential to meet strict timing requirements as well as functional specifications. The correct design of such MC systems requires a thorough understanding of the system's functions and their importance to the system. A failure/deadline miss in functions with various criticality levels has a different impact on the system, from no effect to catastrophic consequences. Failure in the execution of tasks with higher criticality levels (HC tasks) may lead to system failure and cause irreparable damage to the system, while although Low-Criticality (LC) tasks assist the system in carrying out its mission successfully, their failure has less impact on the system's functionality and does not harm the system itself to fail. In order to guarantee the MC system safety, tasks are analyzed with different assumptions to obtain different Worst-Case Execution Times (WCETs) corresponding to the multiple criticality levels and the operation mode of the system. If the execution time of at least one HC task exceeds its low WCET, the system switches from low-criticality mode (LO mode) to high-criticality mode (HI mode). Then, all HC tasks continue executing by considering the high WCET to guarantee the system's safety. In this HI mode, all or some LC tasks are dropped/degraded in favor of HC tasks to ensure HC tasks' correct execution. Determining an appropriate low WCET for each HC task is crucial in designing efficient MC systems and ensuring QoS maximization. However, in the case where the low WCETs are set correctly, it is not recommended to drop/degrade the LC tasks in the HI mode due to its negative impact on the other functions or on the entire system in accomplishing its mission correctly. Therefore, how to analyze the task dropping in the HI mode is a significant challenge in designing efficient MC systems that must be considered to guarantee the successful execution of all HC tasks to prevent catastrophic damages while improving the QoS. Due to the continuous rise in computational demand for MC tasks in safety-critical applications, like controlling autonomous driving, the designers are motivated to deploy MC applications on multi-core platforms. Although the parallel execution feature of multi-core platforms helps to improve QoS and ensures the real-timeliness, high power consumption and temperature of cores may make the system more susceptible to failures and instability, which is not desirable in MC applications. Therefore, improving the QoS while managing the power consumption and guaranteeing real-time constraints is the critical issue in designing such MC systems in multi-core platforms. This thesis addresses the challenges associated with efficient MC system design. We first focus on application analysis by determining the appropriate WCET by proposing a novel approach to provide a reasonable trade-off between the number of scheduled LC tasks at design-time and the probability of mode switching at run-time to improve the system utilization and QoS. The approach presents an analytic-based scheme to obtain low WCETs based on the Chebyshev theorem at design-time. We also show the relationship between the low WCETs and mode switching probability, and formulate and solve the problem for improving resource utilization and reducing the mode switching probability. Further, we analyze the LC task dropping in the HI mode to improve QoS. We first propose a heuristic in which a new metric is defined that determines the number of allowable drops in the HI mode. Then, the task schedulability analysis is developed based on the new metric. Since the occurrence of the worst-case scenario at run-time is a rare event, a learning-based drop-aware task scheduling mechanism is then proposed, which carefully monitors the alterations in the behavior of MC systems at run-time to exploit the dynamic slacks for improving the QoS. Another critical design challenge is how to improve QoS using the parallel feature of multi-core platforms while managing the power consumption and temperature of these platforms. We develop a tree of possible task mapping and scheduling at design-time to cover all possible scenarios of task overrunning and reduce the LC task drop rate in the HI mode while managing the power and temperature in each scenario of task scheduling. Since the dynamic slack is generated due to the early execution of tasks at run-time, we propose an online approach to reduce the power consumption and maximum temperature by using low-power techniques like DVFS and task re-mapping, while preserving the QoS. Specifically, our approach examines multiple tasks ahead to determine the most appropriate task for the slack assignment that has the most significant effect on power consumption and temperature. However, changing the frequency and selecting a proper task for slack assignment and a suitable core for task re-mapping at run-time can be time-consuming and may cause deadline violation. Therefore, we analyze and optimize the run-time scheduler.:1. Introduction 1.1. Mixed-Criticality Application Design 1.2. Mixed-Criticality Hardware Design 1.3. Certain Challenges and Questions 1.4. Thesis Key Contributions 1.4.1. Application Analysis and Modeling 1.4.2. Multi-Core Mixed-Criticality System Design 1.5. Thesis Overview 2. Preliminaries and Literature Reviews 2.1. Preliminaries 2.1.1. Mixed-Criticality Systems 2.1.2. Fault-Tolerance, Fault Model and Safety Requirements 2.1.3. Hardware Architectural Modeling 2.1.4. Low-Power Techniques and Power Consumption Model 2.2. Related Works 2.2.1. Mixed-Criticality Task Scheduling Mechanisms 2.2.2. QoS Improvement Methods in Mixed-Criticality Systems 2.2.3. QoS-Aware Power and Thermal Management in Multi-Core Mixed-Criticality Systems 2.3. Conclusion 3. Bounding Time in Mixed-Criticality Systems 3.1. BOT-MICS: A Design-Time WCET Adjustment Approach 3.1.1. Motivational Example 3.1.2. BOT-MICS in Detail 3.1.3. Evaluation 3.2. A Run-Time WCET Adjustment Approach 3.2.1. Motivational Example 3.2.2. ADAPTIVE in Detail 3.2.3. Evaluation 3.3. Conclusion 4. Safety- and Task-Drop-Aware Mixed-Criticality Task Scheduling 4.1. Problem Objectives and Motivational Example 4.2. FANTOM in detail 4.2.1. Safety Quantification 4.2.2. MC Tasks Utilization Bounds Definition 4.2.3. Scheduling Analysis 4.2.4. System Upper Bound Utilization 4.2.5. A General Design Time Scheduling Algorithm 4.3. Evaluation 4.3.1. Evaluation with Real-Life Benchmarks 4.3.2. Evaluation with Synthetic Task Sets 4.4. Conclusion 5. Learning-Based Drop-Aware Mixed-Criticality Task Scheduling 5.1. Motivational Example and Problem Statement 5.2. Proposed Method in Detail 5.2.1. An Overview of the Design-Time Approach 5.2.2. Run-Time Approach: Employment of SOLID 5.2.3. LIQUID Approach 5.3. Evaluation 5.3.1. Evaluation with Real-Life Benchmarks 5.3.2. Evaluation with Synthetic Task Sets 5.3.3. Investigating the Timing and Memory Overheads of ML Technique 5.4. Conclusion 6. Fault-Tolerance and Power-Aware Multi-Core Mixed-Criticality System Design 6.1. Problem Objectives and Motivational Example 6.2. Design Methodology 6.3. Tree Generation and Fault-Tolerant Scheduling and Mapping 6.3.1. Making Scheduling Tree 6.3.2. Mapping and Scheduling 6.3.3. Time Complexity Analysis 6.3.4. Memory Space Analysis 6.4. Evaluation 6.4.1. Experimental Setup 6.4.2. Analyzing the Tree Construction Time 6.4.3. Analyzing the Run-Time Timing Overhead 6.4.4. Peak Power Management and Thermal Distribution for Real-Life and Synthetic Applications 6.4.5. Analyzing the QoS of LC Tasks 6.4.6. Analyzing the Peak Power Consumption and Maximum Temperature 6.4.7. Effect of Varying Different Parameters on Acceptance Ratio 6.4.8. Investigating Different Approaches at Run-Time 6.5. Conclusion 7. QoS- and Power-Aware Run-Time Scheduler for Multi-Core Mixed-Criticality Systems 7.1. Research Questions, Objectives and Motivational Example 7.2. Design-Time Approach 7.3. Run-Time Mixed-Criticality Scheduler 7.3.1. Selecting the Appropriate Task to Assign Slack 7.3.2. Re-Mapping Technique 7.3.3. Run-Time Management Algorithm 7.3.4. DVFS governor in Clustered Multi-Core Platforms 7.4. Run-Time Scheduler Algorithm Optimization 7.5. Evaluation 7.5.1. Experimental Setup 7.5.2. Analyzing the Relevance Between a Core Temperature and Energy Consumption 7.5.3. The Effect of Varying Parameters of Cost Functions 7.5.4. The Optimum Number of Tasks to Look-Ahead and the Effect of Task Re-mapping 7.5.5. The Analysis of Scheduler Timings Overhead on Different Real Platforms 7.5.6. The Latency of Changing Frequency in Real Platform 7.5.7. The Effect of Latency on System Schedulability 7.5.8. The Analysis of the Proposed Method on Peak Power, Energy and Maximum Temperature Improvement 7.5.9. The Analysis of the Proposed Method on Peak power, Energy and Maximum Temperature Improvement in a Multi-Core Platform Based on the ODROID-XU3 Architecture 7.5.10. Evaluation of Running Real MC Task Graph Model (Unmanned Air Vehicle) on Real Platform 7.6. Conclusion 8. Conclusion and Future Work 8.1. Conclusions 8.2. Future Work